In [1]:
import pandas as pd 
import numpy as np
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
df=pd.read_csv('amazon.csv')
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1465 entries, 0 to 1464
Data columns (total 16 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   product_id           1465 non-null   object
 1   product_name         1465 non-null   object
 2   category             1465 non-null   object
 3   discounted_price     1465 non-null   object
 4   actual_price         1465 non-null   object
 5   discount_percentage  1465 non-null   object
 6   rating               1465 non-null   object
 7   rating_count         1463 non-null   object
 8   about_product        1465 non-null   object
 9   user_id              1465 non-null   object
 10  user_name            1465 non-null   object
 11  review_id            1465 non-null   object
 12  review_title         1465 non-null   object
 13  review_content       1465 non-null   object
 14  img_link             1465 non-null   object
 15  product_link         1465 non-null   object
dtypes: object(16)
memory usage: 183.2+ KB
In [2]:
#split user_id and user_name
df['user_id'] = df['user_id'].str.split(',', expand=False)
df['user_name'] = df['user_name'].str.split(',', expand=False)
In [3]:
#divide the data into other rows
df1 = df.explode('user_name', ignore_index=True)
df2 = df.explode('user_id', ignore_index=True)
df1.drop("user_id", axis=1, inplace=True)
df1["user_id"] = df2["user_id"]
df1.isnull().sum()
Out[3]:
product_id              0
product_name            0
category                0
discounted_price        0
actual_price            0
discount_percentage     0
rating                  0
rating_count            2
about_product           0
user_name               0
review_id               0
review_title            0
review_content          0
img_link                0
product_link            0
user_id                12
dtype: int64
In [4]:
#split the categories
df_category = df1['category'].str.split('|', expand = True).rename(columns={0:'category_1', 1:'category_2', 2:'category_3', 3:'category_4',4:'category_5',})
df_category.isnull().sum()
Out[4]:
category_1        0
category_2        0
category_3       60
category_4     1300
category_5     7385
5             10841
6             11411
dtype: int64
In [5]:
#add df_category to df1
df1['main_category'] = df_category['category_1']
df1['sub_category'] = df_category['category_2']
df1['category_3'] = df_category['category_3']
df1['category_4'] = df_category['category_4']
df1['category_5'] = df_category['category_5']
In [6]:
#drop the main category column
df1.drop('category', axis=1, inplace=True)
df1.isnull().sum()
Out[6]:
product_id                0
product_name              0
discounted_price          0
actual_price              0
discount_percentage       0
rating                    0
rating_count              2
about_product             0
user_name                 0
review_id                 0
review_title              0
review_content            0
img_link                  0
product_link              0
user_id                  12
main_category             0
sub_category              0
category_3               60
category_4             1300
category_5             7385
dtype: int64
In [7]:
#checking the table
df1.head()
Out[7]:
product_id product_name discounted_price actual_price discount_percentage rating rating_count about_product user_name review_id review_title review_content img_link product_link user_id main_category sub_category category_3 category_4 category_5
0 B07JW9H4J1 Wayona Nylon Braided USB to Lightning Fast Cha... ₹399 ₹1,099 64% 4.2 24,269 High Compatibility : Compatible With iPhone 12... Manav R3HXWT0LRP0NMF,R2AJM3LFTLZHFO,R6AQJGUP6P86,R1K... Satisfied,Charging is really fast,Value for mo... Looks durable Charging is fine tooNo complains... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Wayona-Braided-WN3LG1-Sy... AG3D6O4STAQKAY2UVGEUV46KN35Q Computers&Accessories Accessories&Peripherals Cables&Accessories Cables USBCables
1 B07JW9H4J1 Wayona Nylon Braided USB to Lightning Fast Cha... ₹399 ₹1,099 64% 4.2 24,269 High Compatibility : Compatible With iPhone 12... Adarsh gupta R3HXWT0LRP0NMF,R2AJM3LFTLZHFO,R6AQJGUP6P86,R1K... Satisfied,Charging is really fast,Value for mo... Looks durable Charging is fine tooNo complains... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Wayona-Braided-WN3LG1-Sy... AHMY5CWJMMK5BJRBBSNLYT3ONILA Computers&Accessories Accessories&Peripherals Cables&Accessories Cables USBCables
2 B07JW9H4J1 Wayona Nylon Braided USB to Lightning Fast Cha... ₹399 ₹1,099 64% 4.2 24,269 High Compatibility : Compatible With iPhone 12... Sundeep R3HXWT0LRP0NMF,R2AJM3LFTLZHFO,R6AQJGUP6P86,R1K... Satisfied,Charging is really fast,Value for mo... Looks durable Charging is fine tooNo complains... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Wayona-Braided-WN3LG1-Sy... AHCTC6ULH4XB6YHDY6PCH2R772LQ Computers&Accessories Accessories&Peripherals Cables&Accessories Cables USBCables
3 B07JW9H4J1 Wayona Nylon Braided USB to Lightning Fast Cha... ₹399 ₹1,099 64% 4.2 24,269 High Compatibility : Compatible With iPhone 12... S.Sayeed Ahmed R3HXWT0LRP0NMF,R2AJM3LFTLZHFO,R6AQJGUP6P86,R1K... Satisfied,Charging is really fast,Value for mo... Looks durable Charging is fine tooNo complains... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Wayona-Braided-WN3LG1-Sy... AGYHHIERNXKA6P5T7CZLXKVPT7IQ Computers&Accessories Accessories&Peripherals Cables&Accessories Cables USBCables
4 B07JW9H4J1 Wayona Nylon Braided USB to Lightning Fast Cha... ₹399 ₹1,099 64% 4.2 24,269 High Compatibility : Compatible With iPhone 12... jaspreet singh R3HXWT0LRP0NMF,R2AJM3LFTLZHFO,R6AQJGUP6P86,R1K... Satisfied,Charging is really fast,Value for mo... Looks durable Charging is fine tooNo complains... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Wayona-Braided-WN3LG1-Sy... AG4OGOFWXJZTQ2HKYIOCOY3KXF2Q Computers&Accessories Accessories&Peripherals Cables&Accessories Cables USBCables
In [8]:
#Separate words from main and sub category
df1['main_category'] = df1['main_category'].str.replace(',', ' , ')
df1['main_category'] = df1['main_category'].str.replace('&', ' & ')

df1['sub_category'] = df1['sub_category'].str.replace(',', ' , ')
df1['sub_category'] = df1['sub_category'].str.replace('&', ' & ')
In [9]:
#checking the values of rating column
df1["rating"].value_counts()
Out[9]:
4.1    1914
4.2    1813
4.3    1804
4.0    1033
3.9     979
4.4     966
3.8     683
4.5     590
4       412
3.7     325
3.6     274
3.5     207
4.6     126
3.3     120
3.4      80
4.7      45
3.1      25
4.8      19
3.0      17
2.8      16
3.2      14
5.0      14
|         8
3         8
2.6       8
2.9       8
2.3       5
2         2
Name: rating, dtype: int64
In [10]:
#shows the "|" values
df1.query('rating=="|"')
Out[10]:
product_id product_name discounted_price actual_price discount_percentage rating rating_count about_product user_name review_id review_title review_content img_link product_link user_id main_category sub_category category_3 category_4 category_5
10065 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Divya R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AEASTV5BKJJIYW6WVS6JUBSK4MHA Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10066 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Dr Nefario R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AHQ7UT4SYDQMQB6DJDBVVHQBCXXA Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10067 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Deekshith R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AEZFDGHBWLHUXOLDPVNS3UERDNSQ Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10068 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Preeti R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AFPMGJN4SHWHD3DBQBS2FXGBZ6TA Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10069 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Prasanth R R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AGDD5ACY3AGTMTVBQOC3DMUR6REA Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10070 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Pradeep kashiram Tetgure. R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AFHT3WYWI4DB6Z42VVJZQGFFNIZA Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10071 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Abhijin Janardhan R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AFSHYGNQHXNKBEXS62GRETNGH3GQ Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
10072 B08L12N5H1 Eureka Forbes car Vac 100 Watts Powerful Sucti... ₹2,099 ₹2,499 16% | 992 No Installation is provided for this product|1... Prashant R2KKTKM4M9RDVJ,R1O692MZOBTE79,R2WRSEWL56SOS4,R... Decent product,doesn't pick up sand,Ok ok,Must... Does the job well,doesn't work on sand. though... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Eureka-Forbes-Vacuum-Cle... AEZVJENT2FC3K3MKMIB4ZXDWNDPA Home & Kitchen Kitchen & HomeAppliances Vacuum,Cleaning&Ironing Vacuums&FloorCare Vacuums
In [11]:
#change the "|" to 4.0 based on main website
df1
df1['rating']=df1['rating'].str.replace('|', '4.0').astype(float)

C:\Users\Ryzen\AppData\Local\Temp\ipykernel_12792\3942109306.py:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.
  df1['rating']=df1['rating'].str.replace('|', '4.0').astype(float)
In [12]:
df1.shape
Out[12]:
(11515, 20)
In [13]:
#checking the Null rating_count
df1[df1['rating_count'].isnull()==True]
Out[13]:
product_id product_name discounted_price actual_price discount_percentage rating rating_count about_product user_name review_id review_title review_content img_link product_link user_id main_category sub_category category_3 category_4 category_5
2229 B0B94JPY2N Amazon Brand - Solimo 65W Fast Charging Braide... ₹199 ₹999 80% 3.0 NaN USB C to C Cable: This cable has type C connec... Pranav RUB7U91HVZ30 The cable works but is not 65W as advertised I have a pd supported car charger and I bought... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Amazon-Brand-Charging-Su... AG5VQTV5OVY2Q42ZQPWXTRU2PSLQ Computers & Accessories Accessories & Peripherals Cables&Accessories Cables USBCables
2553 B0BQRJ3C47 REDTECH USB-C to Lightning Cable 3.3FT, [Apple... ₹249 ₹999 75% 5.0 NaN 💎[The Fastest Charge] - This iPhone USB C cabl... Abdul Gafur RQXD5SAMMPC6L Awesome Product Quick delivery.Awesome ProductPacking was good... https://m.media-amazon.com/images/I/31-q0xhaTA... https://www.amazon.in/REDTECH-Lightning-Certif... AFOOUANHTKWSTZRG3HSE3TR7L5CQ Computers & Accessories Accessories & Peripherals Cables&Accessories Cables USBCables
In [14]:
df1['rating_count'].fillna(df1['rating_count'].mode()[0], inplace=True)
In [15]:
df1.isnull().sum()
Out[15]:
product_id                0
product_name              0
discounted_price          0
actual_price              0
discount_percentage       0
rating                    0
rating_count              0
about_product             0
user_name                 0
review_id                 0
review_title              0
review_content            0
img_link                  0
product_link              0
user_id                  12
main_category             0
sub_category              0
category_3               60
category_4             1300
category_5             7385
dtype: int64
In [16]:
#checking the user_id Null values
df1[df1["user_id"].isnull()==True].head()
Out[16]:
product_id product_name discounted_price actual_price discount_percentage rating rating_count about_product user_name review_id review_title review_content img_link product_link user_id main_category sub_category category_3 category_4 category_5
11503 B00J5DYCCA Havells Ventil Air DSP 230mm Exhaust Fan (Pist... ₹1,399 ₹1,890 26% 4.0 8,031 Fan sweep area: 230 MM ; Noise level: (40 - 45... ASHISHGAJBE R39Q2Y79MM9SWK,R3079BG1NIH6MB,R29A31ZELTZNJM,R... Fan Speed is slow,Good quality,Good product,go... I have installed this in my kitchen working fi... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Havells-Ventilair-230mm-... NaN Home & Kitchen Heating , Cooling & AirQuality Fans ExhaustFans None
11504 B00J5DYCCA Havells Ventil Air DSP 230mm Exhaust Fan (Pist... ₹1,399 ₹1,890 26% 4.0 8,031 Fan sweep area: 230 MM ; Noise level: (40 - 45... hirabhai thakarda R39Q2Y79MM9SWK,R3079BG1NIH6MB,R29A31ZELTZNJM,R... Fan Speed is slow,Good quality,Good product,go... I have installed this in my kitchen working fi... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Havells-Ventilair-230mm-... NaN Home & Kitchen Heating , Cooling & AirQuality Fans ExhaustFans None
11505 B00J5DYCCA Havells Ventil Air DSP 230mm Exhaust Fan (Pist... ₹1,399 ₹1,890 26% 4.0 8,031 Fan sweep area: 230 MM ; Noise level: (40 - 45... khageswar R39Q2Y79MM9SWK,R3079BG1NIH6MB,R29A31ZELTZNJM,R... Fan Speed is slow,Good quality,Good product,go... I have installed this in my kitchen working fi... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Havells-Ventilair-230mm-... NaN Home & Kitchen Heating , Cooling & AirQuality Fans ExhaustFans None
11506 B00J5DYCCA Havells Ventil Air DSP 230mm Exhaust Fan (Pist... ₹1,399 ₹1,890 26% 4.0 8,031 Fan sweep area: 230 MM ; Noise level: (40 - 45... A.Ramu R39Q2Y79MM9SWK,R3079BG1NIH6MB,R29A31ZELTZNJM,R... Fan Speed is slow,Good quality,Good product,go... I have installed this in my kitchen working fi... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Havells-Ventilair-230mm-... NaN Home & Kitchen Heating , Cooling & AirQuality Fans ExhaustFans None
11507 B01486F4G6 Borosil Jumbo 1000-Watt Grill Sandwich Maker (... ₹2,863 ₹3,690 22% 4.3 6,987 Brand-Borosil, Specification – 23V ~ 5Hz;1 W... Rajib R20RBRZ0WEUJT9,ROKIFK9R2ISSE,R30EEG2FNJSN5I,R2... Works perfect,Ok good product,Nice Product. Re... It does it job perfectly..only issue is temp c... https://m.media-amazon.com/images/W/WEBP_40237... https://www.amazon.in/Borosil-Jumbo-1000-Watt-... NaN Home & Kitchen Kitchen & HomeAppliances SmallKitchenAppliances SandwichMakers None
In [17]:
#change the NaN data
df1['user_id'].fillna(df1['user_id'].mode()[0], inplace=True)
In [18]:
#Start Visualizing
#Count Products by main and sub 
cat = df1.groupby(['main_category','sub_category']).agg('count').iloc[:,1].rename_axis().reset_index(name='Total Product')
cat = pd.pivot_table(cat, index=['main_category', 'sub_category', 'Total Product'])
cat
Out[18]:
main_category sub_category Total Product
Car & Motorbike CarAccessories 8
Computers & Accessories Accessories & Peripherals 3032
Components 40
ExternalDevices & DataStorage 144
Laptops 8
Monitors 16
NetworkingDevices 272
Printers , Inks & Accessories 88
Tablets 4
Electronics Accessories 112
Cameras & Photography 128
GeneralPurposeBatteries & BatteryChargers 112
Headphones , Earbuds & Accessories 512
HomeAudio 124
HomeTheater , TV & Video 1260
Mobiles & Accessories 1244
PowerAccessories 8
WearableTechnology 596
Health & PersonalCare HomeMedicalSupplies & Equipment 4
Home & Kitchen CraftMaterials 56
Heating , Cooling & AirQuality 894
HomeStorage & Organization 128
Kitchen & Dining 8
Kitchen & HomeAppliances 2429
HomeImprovement Electrical 16
MusicalInstruments Microphones 16
OfficeProducts OfficeElectronics 32
OfficePaperProducts 216
Toys & Games Arts & Crafts 8
In [19]:
#total product by Main and Sub Category
main_cat = df1[['main_category', 'product_id']]
sub_cat = df1[['sub_category', 'product_id']]
main_pivot = pd.pivot_table(main_cat, index=['main_category'], aggfunc = "count")
main_pivot.rename(columns={'product_id': "Total Products"}, inplace=True)
main_pivot.sort_values(by='Total Products', inplace=True)

sub_pivot = pd.pivot_table(sub_cat, index=['sub_category'], aggfunc = "count")
sub_pivot.rename(columns={'product_id': "Total Products"}, inplace=True)
sub_pivot.sort_values(by='Total Products', inplace=True)

fig_cat = px.bar(main_pivot, y=main_pivot.index, x='Total Products')
fig_cat.update_layout(title = "Total Number of Products by Main Category", xaxis_title = "Count")

fig_cat2 = px.bar(sub_pivot, y=sub_pivot.index, x='Total Products')
fig_cat2.update_layout(title = "Total Number of Products by Sub Category", xaxis_title = "Count")

fig_cat.show()
fig_cat2.show()
In [20]:
#make a table for category pie chart
main_cat_table = df1['main_category'].value_counts().rename_axis('Main Category').reset_index(name="Total Counts")
sub_cat_table = df1['sub_category'].value_counts().rename_axis('Sub Category').reset_index(name="Total Counts")
main_cat_table
sub_cat_table
Out[20]:
Sub Category Total Counts
0 Accessories & Peripherals 3032
1 Kitchen & HomeAppliances 2429
2 HomeTheater , TV & Video 1260
3 Mobiles & Accessories 1244
4 Heating , Cooling & AirQuality 894
5 WearableTechnology 596
6 Headphones , Earbuds & Accessories 512
7 NetworkingDevices 272
8 OfficePaperProducts 216
9 ExternalDevices & DataStorage 144
10 Cameras & Photography 128
11 HomeStorage & Organization 128
12 HomeAudio 124
13 GeneralPurposeBatteries & BatteryChargers 112
14 Accessories 112
15 Printers , Inks & Accessories 88
16 CraftMaterials 56
17 Components 40
18 OfficeElectronics 32
19 Electrical 16
20 Monitors 16
21 Microphones 16
22 Arts & Crafts 8
23 PowerAccessories 8
24 Laptops 8
25 Kitchen & Dining 8
26 CarAccessories 8
27 Tablets 4
28 HomeMedicalSupplies & Equipment 4
In [21]:
fig_mc = px.pie(main_cat_table, values='Total Counts', names='Main Category', hole=0.5)
fig_mc.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig_mc.update_layout(title="Main Category Donut Chart")

fig_sc = px.pie(sub_cat_table, values='Total Counts', names='Sub Category', hole=0.5)
fig_sc.update_traces(textposition = 'inside', textinfo = 'percent+label')
fig_sc.update_layout(title="Main Category Donut Chart")

fig_mc.show()
fig_sc.show()
In [22]:
#rating distribution
fig3 = px.histogram(df1, x = 'rating')
fig3.update_layout (title='Rating Distribution',
                    xaxis_title = 'rating')
fig3.show()
In [23]:
#Classify Customer Satisfaction by rating
customer_satisfaction = []

for s in df1['rating']:
    if s < 2.0: customer_satisfaction.append('Very Dissatisfied')
    elif s < 3.0: customer_satisfaction.append('Dissatisfied')
    elif s < 4.0: customer_satisfaction.append('Neutral')
    elif s < 5.0: customer_satisfaction.append('Satisfied')
    elif s == 5.0: customer_satisfaction.append('Very Satisfied')
In [24]:
df1['customer_satisfaction'] = customer_satisfaction
df1['customer_satisfaction'] = df1['customer_satisfaction'].astype('category')
In [25]:
fig_ = px.histogram(df1, x = 'rating')
fig3.update_layout (title='Rating Distribution',
                    xaxis_title = 'rating')
fig3.show()
In [26]:
#Visualizing customer_satisfaction  
fig4 = px.histogram(df1, x = 'customer_satisfaction')
fig4.update_layout (title='Customer Satisfaction',
                    xaxis_title = 'Rating', yaxis_title = "Total Ratings")
fig4.show()
In [27]:
#make a table for satisfaction pie chart
customer_satisfaction_table = df1['customer_satisfaction'].value_counts().rename_axis('Customer Satisfaction').reset_index(name="Total Counts")
customer_satisfaction_table
Out[27]:
Customer Satisfaction Total Counts
0 Satisfied 8730
1 Neutral 2732
2 Dissatisfied 39
3 Very Satisfied 14
In [28]:
fig5 = px.pie(customer_satisfaction_table, values='Total Counts', names='Customer Satisfaction', hole=0.5)
fig5.update_layout(title="Customer Satisfaction Percentage")
fig5.show()
In [ ]:
In [ ]: